Financial Data Analytics with R
◆Taylor & Francis セール開催中!:2024年12月22日(日)ご注文分まで
※上記表示の販売価格は割引適用後の価格です 出版済み 3-5週間でお届けいたします。 Monte-Carlo Validation Author: Chen, Jenny K. (Morgan Stanley, U.S.A) Publisher: Taylor & Francis ISBN: 9781032745114 Cover: HARDCOVER Date: 2024年07月 DESCRIPTION Financial Data Analysis with R: Monte-Carlo Validation is a comprehensive exploration of statistical methodologies and their applications in finance. Readers are taken on a journey in each chapter through practical explanations and examples, enabling them to develop a solid foundation of these methods in R and their applications in finance. This book serves as an indispensable resource for finance professionals, analysts, and enthusiasts seeking to harness the power of data-driven decision-making. The book goes beyond just teaching statistical methods in R and incorporates a unique section of informative Monte-Carlo simulations. These Monte-Carlo simulations are uniquely designed to showcase the reader the potential consequences and misleading conclusions that can arise when fundamental model assumptions are violated. Through step-by-step tutorials and realworld cases, readers will learn how and why model assumptions are important to follow. With a focus on practicality, Financial Data Analysis with R: Monte-Carlo Validation equips readers with the skills to construct and validate financial models using R. The Monte-Carlo simulation exercises provide a unique opportunity to understand the methods further, making this book an essential tool for anyone involved in financial analysis, investment strategy, or risk management. Whether you are a seasoned professional or a newcomer to the world of financial analytics, this book serves as a guiding light, empowering you to navigate the landscape of finance with precision and confidence. Key Features: * An extensive compilation of commonly used financial data analytics methods from fundamental to advanced levels * Learn how to model and analyze financial data with step-by-step illustrations in R and ready-to-use publicly available data * Includes Monte-Carlo simulations uniquely designed to showcase the reader the potential consequences and misleading conclusions that arise when fundamental model assumptions are violated * Data and computer programs are available for readers to replicate and implement the models and methods themselves TABLE OF CONTENTS 1. Introduction to R 2. Linear Regression 3. Transition from Linear to Nonlinear Regression 4. Nonlinear Regression Modeling 5. The Logistic Regression 6. The Poisson Regression: Models for Count Data 7. Autoregressive Integrated Moving-Average Models 8. Generalized AutoRegressive Conditional Heteroskedasticity Model 9. Cointegration 10. Financial Statistical Modeling in Risk and Wealth Management Bibliography
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